code: def compute_supervision_coarse(
      data: dict[str, Any], config: dict[str, Any]
  ) -> None:
      if len(set(data["dataset_name"])) != 1:
          raise ValueError("Do not support mixed datasets training!")
      data_source = data["dataset_name"][0]
      if data_source.lower() in ["scannet", "megadepth"]:
          spvs_coarse(data, config)
      else:
          raise ValueError(f"Unknown data source: {data_source}")
  
file_path: kornia/kornia/feature/loftr/utils/supervision.py
imports: from __future__ import annotations
  from typing import Any
  import torch
  from kornia.core import Tensor
  from kornia.utils import create_meshgrid
  from .geometry import warp_kpts
label_desc: Use image node_id 3 for calling a global function with example usage: compute_supervision_coarse(data, config) without return types